External Evaluation of Population Pharmacokinetic Models and Bayes-Based Dosing of Infliximab

Despite the well-demonstrated efficacy of infliximab in inflammatory diseases, treatment failure remains frequent. Dose adjustment using Bayesian methods has shown in silico its interest in achieving target plasma concentrations. However, most of the published models have not been fully validated in accordance with the recommendations. This study aimed to submit these models to an external evaluation and verify their predictive capabilities. Eight models were selected for external evaluation, carried out on an independent database (409 concentrations from 157 patients). Each model was evaluated based on the following parameters: goodness-of-fit (comparison of predictions to observations), residual error model (population weighted residuals (PWRES), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE)), and predictive performances (prediction-corrected visual predictive checks (pcVPC) and Bayesian simulations). The performances observed during this external evaluation varied greatly from one model to another. The eight evaluated models showed a significant bias in population predictions (from −7.19 to 7.38 mg/L). Individual predictions showed acceptable bias and precision for six of the eight models (mean error of −0.74 to −0.29 mg/L and mean percent error of −16.6 to −0.4%). Analysis of NPDE and pcVPC confirmed these results and revealed a problem with the inclusion of several covariates (weight, concomitant immunomodulatory treatment, presence of anti-drug antibodies). This external evaluation showed satisfactory results for some models, notably models A and B, and highlighted several prospects for improving the pharmacokinetic models of infliximab for clinical-biological application.

INFLUENCE OF THE MAIN FACTORS ON THE CONDITIONS OF FORMATION OF THE ACHIMOV FORMATION

Western Siberia is a unique geological area for testing different approaches of sequential stratigraphy and their impact on deep-sea sediment architecture. There are many different various techniques allow us to reduce geological uncertainty and predict distribution and architecture of deep-water sediments. The architecture of deepwater sediments is influenced by the basin topography and volume of sediment supply. In this paper the main factors and their influence on the deep-water sediments accumulation will be considered. This approach was used for prediction distribution and quality of deep-water sediments in poorly studied areas. Progradation and retrogradation trends, topography of basin floor and shelf`s edge trajectorywere analyzed to determine an effect. Based on empirical estimation, it has been determined that the most important factor is the trajectory of change of shelf`s edge and topography. There was no apparent correlation between the quality of deep-water sediments and the amount of volume sediment supply. All of this data and correlation was used for estimation potential of the achimov formation in poorly studied areas.

Pharmacokinetic modeling of intravenous sildenafil in newborns with congenital diaphragmatic hernia

Purpose We developed a pharmacokinetic model of intravenous sildenafil in newborns with congenital diaphragmatic hernia (CDH) to achieve a target plasma concentration of over 50 μg/l. Methods Twenty-three CDH newborns with pulmonary hypertension (64 blood samples) received intravenous sildenafil. Patients received a loading dose of 0.35 mg/kg (IQR 0.16 mg/kg) for 3 h, followed by a continuous infusion of 1.5 mg/kg/day (IQR 0.1 mg/kg/day). For model development, non-linear mixed modeling was used. Inter-individual variability (IIV) and inter-occasion variability were tested. Demographic and laboratory parameters were evaluated as covariates. Normalized prediction distribution errors (NPDE) and visual predictive check (VPC) were used for model validation. Results A two-compartment disposition model of sildenafil and a one-compartment disposition model of desmethyl sildenafil (DMS) was observed with IIV in sildenafil and DMS clearance and volume of distribution of sildenafil. NPDE and VPC revealed adequate predictability. Only postnatal age increased sildenafil clearance. This was partly compensated by a higher DMS concentration, which also has a therapeutic effect. In this small group of patients, sildenafil was tolerated well. Conclusions This model for sildenafil in CDH patients shows that concentration-targeted sildenafil dosing of 0.4 mg/kg in 3 h, followed by 1.6 mg/kg/day continuous infusion achieves appropriate sildenafil plasma levels.

External evaluation of population pharmacokinetic models for vancomycin in neonates

IntroductionNumerous vancomycin population pharmacokinetic (PK) models of neonates have been published. We aimed to comparatively evaluate a set of these models by quantifying their model-based and Bayesian concentration prediction performances using an external retrospective dataset, and estimate their attainment rates in predefined therapeutic target ranges.MethodsImplementations of 12 published PK models were added in the Bayesian dose optimisation tool, DosOpt. Model based concentration predictions informed by variable number of individual concentrations were evaluated using multiple error metrics. A simulation study assessed the probabilities of target attainment (PTA) in trough concentration target ranges 10–15 mg/L and 10–20 mg/L.ResultsNormalized prediction distribution error analysis revealed external validation dataset discordances (global P < 0.05) with all population PK models. Inclusion of a single concentration improved both precision and accuracy. The model by Marques-Minana et al. (2010) attained 68% of predictions within 30% of true concentrations. Absolute percentage errors of most models were within 20-30%. Mean PTA with Zhao et al. (2013) was 40.4% [coefficient-of-variation (CV) 0.5%] and 62.9% (CV 0.4%) within 10–15 mg/L and 10–20 mg/L, respectively.ConclusionPredictive performances varied widely between models. Population based predictions were discordant with external validation dataset but Bayesian modelling with individual concentrations improved both precision and accuracy. Current vancomycin PK models achieve relatively low attainment of commonly recommended therapeutic target ranges.

External Evaluation of Two Fluconazole Infant Population Pharmacokinetic Models

ABSTRACT Fluconazole is an antifungal agent used for the treatment of invasive candidiasis, a leading cause of morbidity and mortality in premature infants. Population pharmacokinetic (PK) models of fluconazole in infants have been previously published by Wade et al. (Antimicrob Agents Chemother 52:4043–4049, 2008, https://doi.org/10.1128/AAC.00569-08 ) and Momper et al. (Antimicrob Agents Chemother 60:5539–5545, 2016, https://doi.org/10.1128/AAC.00963-16 ). Here we report the results of the first external evaluation of the predictive performance of both models. We used patient-level data from both studies to externally evaluate both PK models. The predictive performance of each model was evaluated using the model prediction error (PE), mean prediction error (MPE), mean absolute prediction error (MAPE), prediction-corrected visual predictive check (pcVPC), and normalized prediction distribution errors (NPDE). The values of the parameters of each model were reestimated using both the external and merged data sets. When evaluated with the external data set, the model proposed by Wade et al. showed lower median PE, MPE, and MAPE (0.429 μg/ml, 41.9%, and 57.6%, respectively) than the model proposed by Momper et al. (2.45 μg/ml, 188%, and 195%, respectively). The values of the majority of reestimated parameters were within 20% of their respective original parameter values for all model evaluations. Our analysis determined that though both models are robust, the model proposed by Wade et al. had greater accuracy and precision than the model proposed by Momper et al., likely because it was derived from a patient population with a wider age range. This study highlights the importance of the external evaluation of infant population PK models.

PROSPECTIVE VALIDATION OF A MODEL-BASED DOSING REGIMEN FOR AMIKACIN IN NEONATES: FEASIBLE AND RELEVANT

IntroductionA neonatal amikacin dosing regimen was previously developed based on a population pharmacokinetic model. The aim of the current study was to prospectively validate this model-derived dosing regimen.MethodsFirst, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (<3 mg/L) and peak (>24 mg/L) levels. Secondly, observed concentrations were compared with model-predicted concentrations, whereby the results of an NPDE (normalized prediction distribution error) were considered as well. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (prescription errors and disease characteristics of outliers) were explored.ResultsIn 579 neonates [median (range) birth bodyweight 2285 (420–4850) g, postnatal age 2 (1–30) days, gestational age 34 (24–41) weeks], 90.5% of early peak levels reached 24 mg/L and 60.2% of trough levels was <3 mg/L (93.4% ≤5 mg/L). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations >24 mg/L were reached in almost all patients. Trough values <3 mg/L were documented in 78–100% and 45–96% of simulated cases, respectively, when ibuprofen was co-administered or not. Suboptimal trough levels were found in patient subgroups with postnatal age <14 days and current weight >2000g.ConclusionsProspective validation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Adapted dosing for patients with suboptimal trough levels was proposed. Besides improving dosing individualization, feasibility and relevance of neonatal prospective validation studies was demonstrated.